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Organizations are experiencing a shift in how they operate across industries and verticals. This includes the manufacturing sector, which is undergoing its own digital transformation as traditional factory processes evolve to create the factory of the future.
When it comes to manufacturing, there’s nothing more time-consuming or costly than a breakdown on the assembly line. Idle time on the line impacts everything from employee productivity to the supply chain, and it’s often difficult to make that time up even when the machinery is back up and running.
To mitigate these kinds of issues, many manufacturers are looking to technology in an effort to become a smart factory, as they operate in what’s known as the Fourth Industrial Revolution, or Industry 4.0.
A smart factory is modernized with technologies, such as Internet of Things (IoT), artificial intelligence (AI), and cloud-based computing to drive innovation and productivity. These technologies turn a reactive response into a proactive one, by identifying and recommending solutions for issues before they ever occur, using real-time analytics, data-driven decision making, machine learning, and a more complete visibility into your controls.
Here are three factors to consider when evolving your manufacturing operation into a factory of the future, a smart factory.
1. You don’t know what you don’t know.
What many manufacturers discover after implementing sensors into their machinery is how much they were operating blindly. While warning signs accumulate over time (and we’ll talk about the experts who can recognize them quickly in the next point), more often than not a breakdown can come as an unwelcome surprise. By adding sensors to machinery, they can read, capture, and analyze data in order to more accurately predict issues before they occur, not just on a single machine, but on a group of them.
In this new world, if the temperature bumps up on one machine, and RPMs increase on another, it can predict what that combination of effects can have over time and flag issues before they cause damage. Or, if a machine’s temperature gradually raises over a number of days, an analysis can flag that the last time that happened, the machine shut down three weeks later. Preventing these slowdowns or complete stoppages creates huge cost savings over a relatively short period of time.
2. Technology doesn’t replace people.
The introduction of technologies such as IoT or AI into an organization can sometimes trigger a knee-jerk reaction of fear and resentment amongst employees. The fact is that these technologies aren’t replacements for your workforce. Rather, they’re like adding valuable members to your team. That one guru that likely exists on your team who can predict a machine breakdown more accurately than the weather isn’t going to be replaced by a sensor that can identify an anomaly through monitoring. Instead, the combination of human knowledge and machine learning can make analysis even better. When implemented correctly, technology makes people more efficient and focused on more valuable tasks.
3. Prevention beats a cure.
While this is the case in many things, it’s often human nature to wait until there’s a compelling event to make a change. The fact is that if you’re not making a move to become a smart factory, you’ll be stuck operating as a factory of the past. In today’s highly competitive global market, those who are able to maximize their yield, improve their quality control, and keep their systems running are going to win the race.
How do you get started?
- Prioritize machinery within your manufacturing process that has the most likelihood of breaking down. Often, manufacturers start with high-value assets as those can be the costliest when they go down.
- Ensure the data coming from your sensors is properly captured and stored. Retrofit your line for data capture with IoT sensors for key data points if they are not already there.
- Work with an expert to come in and set up a cloud-based data environment, which can take the data in and connect readings together. Additionally, they can build KPIs, reports, and machine learning on top of it.
- Once the data is captured, put it into cloud storage where it can be used for real-time analytics, predictive analytics, and machine learning.
At Quisitive, we work with many manufacturers in helping them become smart factories. Learn more about our On-Ramp to Azure Data methodology here.
